Introduction

In this report, we extract information about published JOSS papers and generate graphics as well as a summary table that can be downloaded and used for further analyses.

Load required R packages

suppressPackageStartupMessages({
  library(tibble)
  library(rcrossref)
  library(dplyr)
  library(tidyr)
  library(ggplot2)
  library(lubridate)
  library(gh)
  library(purrr)
  library(jsonlite)
  library(DT)
  library(plotly)
})

Collect information about papers

Pull down papers and citation info from Crossref

We get the information about published JOSS papers from Crossref, using the rcrossref R package. This package is also used to extract citation counts.

## Fetch JOSS papers from Crossref
## Only 1000 papers at the time can be pulled down
lim <- 1000
papers <- rcrossref::cr_works(filter = c(issn = "2475-9066"), 
                              limit = lim)$data

## Warning: `tbl_df()` is deprecated as of dplyr 1.0.0.
## Please use `tibble::as_tibble()` instead.
## [90mThis warning is displayed once every 8 hours.[39m
## [90mCall `lifecycle::last_warnings()` to see where this warning was generated.[39m

i <- 1
while (nrow(papers) == i * lim) {
  papers <- dplyr::bind_rows(
    papers, 
    rcrossref::cr_works(filter = c(issn = "2475-9066"), 
                        limit = lim, offset = i * lim)$data)
  i <- i + 1
}
papers <- papers %>%
  dplyr::filter(type == "journal-article") 

## A few papers don't have DOIs - generate them from the URL
noaltid <- which(is.na(papers$alternative.id))
papers$alternative.id[noaltid] <- gsub("http://dx.doi.org/", "",
                                       papers$url[noaltid])

## Get citation info from Crossref and merge with paper details
cit <- rcrossref::cr_citation_count(doi = papers$alternative.id)
papers <- papers %>% dplyr::left_join(
  cit %>% dplyr::rename(citation_count = count), 
  by = c("alternative.id" = "doi")
)

## Remove one duplicated paper
papers <- papers %>% dplyr::filter(alternative.id != "10.21105/joss.00688")

Pull down info from Whedon API

For each published paper, we use the Whedon API to get information about pre-review and review issue numbers, corresponding software repository etc.

whedon <- list()
p <- 1
a <- jsonlite::fromJSON(
  url(paste0("https://joss.theoj.org/papers/published.json?page=", p)),
  simplifyDataFrame = FALSE
)
while (length(a) > 0) {
  whedon <- c(whedon, a)
  p <- p + 1
  a <- jsonlite::fromJSON(
    url(paste0("https://joss.theoj.org/papers/published.json?page=", p)),
    simplifyDataFrame = FALSE
  )
}

whedon <- do.call(dplyr::bind_rows, lapply(whedon, function(w) {
  data.frame(api_title = w$title, 
             api_state = w$state,
             repo_url = w$repository_url,
             review_issue_id = w$review_issue_id,
             doi = w$doi,
             prereview_issue_id = ifelse(!is.null(w$meta_review_issue_id),
                                         w$meta_review_issue_id, NA_integer_),
             languages = paste(w$metadata$paper$languages, collapse = ","),
             archive_doi = w$metadata$paper$archive_doi)
}))

papers <- papers %>% dplyr::left_join(whedon, by = c("alternative.id" = "doi"))

Combine with info from GitHub issues

From each pre-review and review issue, we extract information about review times and assigned labels.

## Pull down info on all issues in the joss-reviews repository
issues <- gh("/repos/openjournals/joss-reviews/issues", 
             .limit = 5000, state = "all")

## From each issue, extract required information
iss <- do.call(dplyr::bind_rows, lapply(issues, function(i) {
  data.frame(title = i$title, 
             number = i$number,
             state = i$state,
             opened = i$created_at,
             closed = ifelse(!is.null(i$closed_at),
                             i$closed_at, NA_character_),
             ncomments = i$comments,
             labels = paste(setdiff(
               vapply(i$labels, getElement, 
                      name = "name", character(1L)),
               c("review", "pre-review", "query-scope", "paused")),
               collapse = ","))
}))

## Split into REVIEW, PRE-REVIEW, and other issues (the latter category 
## is discarded)
issother <- iss %>% dplyr::filter(!grepl("\\[PRE REVIEW\\]", title) & 
                                    !grepl("\\[REVIEW\\]", title))
dim(issother)

## [1] 26  7

head(issother)

##                                                       title number  state
## 1 @torressa @poulson I only found a couple of small issues:   2082 closed
## 2                         Request to regenerate final proof   2045 closed
## 3                     issues running example program Karate   2015 closed
## 4                                          @whedon commands   1898 closed
## 5                                                   @whedon   1897 closed
## 6                                    Updated issue template   1510 closed
##                 opened               closed ncomments labels
## 1 2020-02-07T09:51:50Z 2020-02-07T09:52:09Z         2       
## 2 2020-01-28T14:44:07Z 2020-01-28T14:45:26Z         2       
## 3 2020-01-15T13:25:37Z 2020-01-15T15:05:18Z         3       
## 4 2019-11-17T09:44:23Z 2019-11-17T10:26:41Z         4       
## 5 2019-11-17T09:43:49Z 2019-11-17T10:26:30Z         4       
## 6 2019-06-18T19:13:46Z 2019-06-18T19:43:30Z         0

## For REVIEW issues, generate the DOI of the paper from the issue number
getnbrzeros <- function(s) {
  paste(rep(0, 5 - nchar(s)), collapse = "")
}
issrev <- iss %>% dplyr::filter(grepl("\\[REVIEW\\]", title)) %>%
  dplyr::mutate(nbrzeros = purrr::map_chr(number, getnbrzeros)) %>%
  dplyr::mutate(alternative.id = paste0("10.21105/joss.", 
                                        nbrzeros,
                                        number)) %>%
  dplyr::select(-nbrzeros) %>% 
  dplyr::mutate(title = gsub("\\[REVIEW\\]: ", "", title)) %>%
  dplyr::rename_at(vars(-alternative.id), ~ paste0("review_", .))

## For PRE-REVIEW issues, add information about the corresponding REVIEW 
## issue number
isspre <- iss %>% dplyr::filter(grepl("\\[PRE REVIEW\\]", title)) %>%
  dplyr::filter(!grepl("withdrawn", labels)) %>%
  dplyr::filter(!grepl("rejected", labels))
## Some titles have multiple pre-review issues. In these cases, keep the latest
isspre <- isspre %>% dplyr::arrange(desc(number)) %>% 
  dplyr::filter(!duplicated(title)) %>% 
  dplyr::mutate(title = gsub("\\[PRE REVIEW\\]: ", "", title)) %>%
  dplyr::rename_all(~ paste0("prerev_", .))

papers <- papers %>% dplyr::left_join(issrev, by = "alternative.id") %>% 
  dplyr::left_join(isspre, by = c("prereview_issue_id" = "prerev_number")) %>%
  dplyr::mutate(prerev_opened = as.Date(prerev_opened),
                prerev_closed = as.Date(prerev_closed),
                review_opened = as.Date(review_opened),
                review_closed = as.Date(review_closed)) %>% 
  dplyr::mutate(days_in_pre = prerev_closed - prerev_opened,
                days_in_rev = review_closed - review_opened,
                to_review = !is.na(review_opened))

Add information from software repositories

software_urls <- papers$repo_url
is_github <- grepl("github", software_urls)
length(is_github)

## [1] 974

sum(is_github)

## [1] 937

software_urls[!is_github]

##  [1] "https://bitbucket.org/cloopsy/android/"                    
##  [2] "https://bitbucket.org/manuela_s/hcp/"                      
##  [3] "https://gitlab.com/celliern/scikit-fdiff/"                 
##  [4] "https://doi.org/10.17605/OSF.IO/3DS6A"                     
##  [5] "https://bitbucket.org/glotzer/rowan"                       
##  [6] "https://gitlab.com/moorepants/skijumpdesign"               
##  [7] "https://gitlab.com/costrouc/pysrim"                        
##  [8] "https://gitlab.com/toposens/public/ros-packages"           
##  [9] "https://gitlab.inria.fr/azais/treex"                       
## [10] "https://bitbucket.org/basicsums/basicsums"                 
## [11] "https://savannah.nongnu.org/projects/complot/"             
## [12] "http://mutabit.com/repos.fossil/grafoscopio/"              
## [13] "https://bitbucket.org/cardosan/brightway2-temporalis"      
## [14] "https://bitbucket.org/cdegroot/wediff"                     
## [15] "https://bitbucket.org/meg/cbcbeat"                         
## [16] "https://bitbucket.org/likask/mofem-cephas"                 
## [17] "https://vcs.ynic.york.ac.uk/analysis/sails"                
## [18] "https://bitbucket.org/ocellarisproject/ocellaris"          
## [19] "https://gitlab.com/QComms/cqptoolkit"                      
## [20] "https://bitbucket.org/dolfin-adjoint/pyadjoint"            
## [21] "https://gitlab.com/dlr-dw/ontocode"                        
## [22] "https://gitlab.com/eidheim/Simple-Web-Server"              
## [23] "https://bitbucket.org/dghoshal/frieda"                     
## [24] "https://gitlab.com/cerfacs/batman"                         
## [25] "https://gitlab.com/tesch1/cppduals"                        
## [26] "https://gitlab.com/gdetor/genetic_alg"                     
## [27] "https://bitbucket.org/hammurabicode/hamx"                  
## [28] "https://gitlab.com/datafold-dev/datafold/"                 
## [29] "https://bitbucket.org/cmutel/brightway2"                   
## [30] "https://gitlab.com/materials-modeling/wulffpack"           
## [31] "https://www.idpoisson.fr/fullswof/"                        
## [32] "https://sourceforge.net/p/mcapl/mcapl_code/ci/master/tree/"
## [33] "https://gricad-gitlab.univ-grenoble-alpes.fr/ttk/spam/"    
## [34] "https://c4science.ch/source/tamaas/"                       
## [35] "https://gitlab.inria.fr/miet/miet"                         
## [36] "https://bitbucket.org/mpi4py/mpi4py-fft"                   
## [37] "https://gitlab.com/myqueue/myqueue"

df <- do.call(dplyr::bind_rows, lapply(software_urls[is_github], function(u) {
  u0 <- gsub("\\.git$", "", gsub("/$", "", u))
  info <- try({
    gh(gsub("(https://)?(www.)?github.com/", "/repos/", u0))
  })
  if (!is(info, "try-error") && length(info) > 1) {
    data.frame(repo_url = u, 
               repo_created = info$created_at,
               repo_updated = info$updated_at,
               repo_pushed = info$pushed_at,
               repo_nbr_stars = info$stargazers_count,
               repo_language = ifelse(!is.null(info$language),
                                      info$language, NA_character_),
               repo_license = ifelse(!is.null(info$license),
                                     info$license$key, NA_character_)
    )
  } else {
    NULL
  }
})) %>%
  dplyr::mutate(repo_created = as.Date(repo_created),
                repo_updated = as.Date(repo_updated),
                repo_pushed = as.Date(repo_pushed)) %>%
  dplyr::distinct()
stopifnot(length(unique(df$repo_url)) == length(df$repo_url))
papers <- papers %>% dplyr::left_join(df, by = "repo_url")

Clean up a bit

## Convert publication date to Date format
## Add information about the half year (H1, H2) of publication
papers <- papers %>% dplyr::select(-reference, -license, -link) %>%
  dplyr::mutate(published.date = as.Date(published.print)) %>% 
  dplyr::mutate(
    halfyear = paste0(year(published.date), 
                      ifelse(month(published.date) <= 6, "H1", "H2"))
  ) %>% dplyr::mutate(
    halfyear = factor(halfyear, 
                      levels = paste0(rep(sort(unique(year(published.date))), 
                                          each = 2), c("H1", "H2")))
  )

Number of published papers per month

ggplot(papers %>% 
         dplyr::mutate(pubmonth = lubridate::floor_date(published.date, "month")) %>%
         dplyr::group_by(pubmonth) %>%
         dplyr::summarize(npub = n()), 
       aes(x = factor(pubmonth), y = npub)) + 
  geom_bar(stat = "identity") + theme_minimal() + 
  labs(x = "", y = "Number of published papers") + 
  theme(axis.title = element_text(size = 15),
        axis.text = element_text(angle = 90, hjust = 1, vjust = 0.5))

Most cited papers

The table below sorts the JOSS papers in decreasing order by the number of citations in Crossref.

DT::datatable(
  papers %>% 
    dplyr::mutate(url = paste0("<a href='", url, "' target='_blank'>", 
                               url,"</a>")) %>% 
    dplyr::arrange(desc(citation_count)) %>% 
    dplyr::select(title, url, published.date, citation_count),
  escape = FALSE,
  options = list(scrollX = TRUE)
)

Citation count vs time since publication

plotly::ggplotly(
  ggplot(papers, aes(x = published.date, y = citation_count, label = title)) + 
    geom_point(alpha = 0.5) + theme_bw() + scale_y_sqrt() + 
    geom_smooth() + 
    labs(x = "Date of publication", y = "Crossref citation count") + 
    theme(axis.title = element_text(size = 15)),
  tooltip = c("label", "x", "y")
)

Power law of citation count within each half year

Here, we plot the citation count for all papers published within each half year, sorted in decreasing order.

ggplot(papers %>% dplyr::group_by(halfyear) %>% 
         dplyr::arrange(desc(citation_count)) %>%
         dplyr::mutate(idx = seq_along(citation_count)), 
       aes(x = idx, y = citation_count)) + 
  geom_point(alpha = 0.5) + 
  facet_wrap(~ halfyear, scales = "free") + 
  theme_bw() + 
  labs(x = "Index", y = "Crossref citation count")

Pre-review/review time over time

In these plots we investigate whether the time a submission spends in the pre-review or review stage has changed over time.

ggplot(papers, aes(x = prerev_opened, y = as.numeric(days_in_pre))) + 
  geom_point() + geom_smooth() + theme_bw() + 
  scale_y_sqrt() + 
  labs(x = "Date of pre-review opening", y = "Number of days in pre-review") + 
  theme(axis.title = element_text(size = 15))

ggplot(papers, aes(x = review_opened, y = as.numeric(days_in_rev))) + 
  geom_point() + geom_smooth() + theme_bw() + 
  scale_y_sqrt() + 
  labs(x = "Date of review opening", y = "Number of days in review") + 
  theme(axis.title = element_text(size = 15))

Languages

Next, we consider the languages used by the submissions. Note that a given submission can use multiple languages.

sspl <- strsplit(papers$languages, ",")
all_languages <- unique(unlist(sspl))
langs <- do.call(dplyr::bind_rows, lapply(all_languages, function(l) {
  data.frame(language = l,
             nbr_submissions = sum(vapply(sspl, function(v) l %in% v, 0)))
}))
langs %>% dplyr::arrange(desc(nbr_submissions))

##            language nbr_submissions
## 1               TeX             830
## 2            Python             548
## 3                 R             259
## 4             Shell             250
## 5          Makefile             179
## 6               C++             142
## 7              HTML             119
## 8  Jupyter Notebook             110
## 9                 C              72
## 10              CSS              64
## 11       JavaScript              54
## 12        Batchfile              53
## 13            CMake              51
## 14           Matlab              45
## 15            Julia              31
## 16          Fortran              24
## 17       PowerShell              18
## 18             Java              18
## 19             Ruby              18
## 20             Perl              11
## 21            Rebol               9
## 22               Go               7
## 23             GLSL               7
## 24               M4               6
## 25               C#               6
## 26             Roff               6
## 27                M               6
## 28             Rust               5
## 29           Prolog               4
## 30             Cuda               4
## 31              PHP               4
## 32           Smarty               4
## 33              IDL               3
## 34            Scala               3
## 35            QMake               3
## 36      Mathematica               3
## 37              Awk               2
## 38             NSIS               2
## 39        Smalltalk               2
## 40         Assembly               2
## 41           Groovy               2
## 42             Stan               2
## 43             XSLT               2
## 44         IGOR Pro               2
## 45              Vue               2
## 46      Objective-C               2
## 47            OCaml               2
## 48              Tcl               2
## 49              GAP               2
## 50              Lua               2
## 51             Mako               1
## 52       AGS Script               1
## 53            PLSQL               1
## 54      Common Lisp               1
## 55               eC               1
## 56            Lasso               1
## 57          Gnuplot               1
## 58            Stata               1
## 59              wdl               1
## 60       Emacs Lisp               1
## 61           Kotlin               1
## 62       Inno Setup               1
## 63            Logos               1
## 64             Yacc               1
## 65     CoffeeScript               1
## 66               F#               1
## 67       FreeMarker               1
## 68           XQuery               1
## 69                D               1
## 70             Golo               1
## 71           Scheme               1
## 72           Puppet               1
## 73            HyPhy               1
## 74     UnrealScript               1
## 75       TypeScript               1
## 76            ANTLR               1
## 77              Max               1
## 78              QML               1
## 79           JSONiq               1

ggplot(langs %>% dplyr::arrange(desc(nbr_submissions)) %>%
         dplyr::mutate(language = factor(language, levels = language)),
       aes(x = language, y = nbr_submissions)) + 
  geom_bar(stat = "identity") + 
  theme_bw() + 
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) + 
  labs(x = "", y = "Number of submissions") + 
  theme(axis.title = element_text(size = 15))

Association between number of citations and number of stars of the GitHub repo

ggplotly(
  ggplot(papers, aes(x = citation_count, y = repo_nbr_stars,
                     label = title)) + 
    geom_point(alpha = 0.5) + scale_x_sqrt() + scale_y_sqrt() + 
    theme_bw() + 
    labs(x = "Crossref citation count", y = "Number of stars, GitHub repo") + 
    theme(axis.title = element_text(size = 15)),
  tooltip = c("label", "x", "y")
)

Distribution of time between GitHub repo creation and JOSS submission

ggplot(papers, aes(x = as.numeric(prerev_opened - repo_created))) +
  geom_histogram(bins = 50) + 
  theme_bw() + 
  labs(x = "Time (days) from repo creation to JOSS pre-review start") + 
  theme(axis.title = element_text(size = 15))

Distribution of time between JOSS acceptance and last commit

ggplot(papers, aes(x = as.numeric(repo_pushed - review_closed))) +
  geom_histogram(bins = 50) + 
  theme_bw() + 
  labs(x = "Time (days) from closure of JOSS review to most recent commit in repo") + 
  theme(axis.title = element_text(size = 15)) + 
  facet_wrap(~ year(published.date), scales = "free_y")

Distribution of software repo licenses

ggplot(papers, aes(x = repo_license)) +
  geom_bar() + 
  theme_bw() + 
  labs(x = "Software license", y = "Number of submissions") + 
  theme(axis.title = element_text(size = 15),
        axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) + 
  facet_wrap(~ year(published.date), scales = "free_y")

## For plots below, replace licenses present in less 
## than 2.5% of the submissions by 'other'
tbl <- table(papers$repo_license)
to_replace <- names(tbl[tbl <= 0.025 * nrow(papers)])

ggplot(papers %>% 
         dplyr::mutate(year = year(published.date)) %>%
         dplyr::mutate(repo_license = replace(repo_license, 
                                              repo_license %in% to_replace,
                                              "other")) %>%
         dplyr::mutate(year = factor(year), repo_license = factor(repo_license)) %>%
         dplyr::group_by(year, repo_license, .drop = FALSE) %>%
         dplyr::count() %>%
         dplyr::mutate(year = as.integer(as.character(year))), 
       aes(x = year, y = n, fill = repo_license)) + geom_area() + 
  theme_minimal() + 
  scale_fill_brewer(palette = "Set1", name = "Software\nlicense", 
                    na.value = "grey") + 
  theme(axis.title = element_text(size = 15)) + 
  labs(x = "Year", y = "Number of submissions")

ggplot(papers %>% 
         dplyr::mutate(year = year(published.date)) %>%
         dplyr::mutate(repo_license = replace(repo_license, 
                                              repo_license %in% to_replace,
                                              "other")) %>%
         dplyr::mutate(year = factor(year), repo_license = factor(repo_license)) %>%
         dplyr::group_by(year, repo_license, .drop = FALSE) %>%
         dplyr::summarize(n = n()) %>%
         dplyr::mutate(freq = n/sum(n)) %>%
         dplyr::mutate(year = as.integer(as.character(year))), 
       aes(x = year, y = freq, fill = repo_license)) + geom_area() + 
  theme_minimal() + 
  scale_fill_brewer(palette = "Set1", name = "Software\nlicense", 
                    na.value = "grey") + 
  theme(axis.title = element_text(size = 15)) + 
  labs(x = "Year", y = "Fraction of submissions")

Save object

The tibble object with all data collected above is serialized to a file that can be downloaded and reused. To read the current version of this file directly from GitHub, use the following code:

papers <- readRDS(gzcon(url("https://github.com/openjournals/joss-analytics/blob/gh-pages/joss_submission_analytics.rds?raw=true")))

head(papers) %>% as.data.frame()

##        alternative.id                     container.title    created  deposited
## 1 10.21105/joss.00900     Journal of Open Source Software 2018-09-23 2018-09-23
## 2 10.21105/joss.01423     Journal of Open Source Software 2019-05-08 2019-11-17
## 3 10.21105/joss.00011 The Journal of Open Source Software 2016-05-12 2017-10-24
## 4 10.21105/joss.01614     Journal of Open Source Software 2019-08-20 2019-11-17
## 5 10.21105/joss.00782     Journal of Open Source Software 2018-06-29 2018-06-29
## 6 10.21105/joss.00082 The Journal of Open Source Software 2016-10-27 2019-09-15
##   published.print                 doi    indexed      issn issue     issued
## 1      2018-09-23 10.21105/joss.00900 2020-03-10 2475-9066    29 2018-09-23
## 2      2019-05-08 10.21105/joss.01423 2020-04-07 2475-9066    37 2019-05-08
## 3      2016-05-11 10.21105/joss.00011 2020-02-21 2475-9066     1 2016-05-11
## 4      2019-08-20 10.21105/joss.01614 2020-02-14 2475-9066    40 2019-08-20
## 5      2018-06-29 10.21105/joss.00782 2020-03-10 2475-9066    26 2018-06-29
## 6      2016-10-27 10.21105/joss.00082 2020-01-30 2475-9066     6 2016-10-27
##   member page   prefix        publisher reference.count score   source
## 1   8722  900 10.21105 The Open Journal               9     1 Crossref
## 2   8722 1423 10.21105 The Open Journal               9     1 Crossref
## 3   8722   11 10.21105 The Open Journal               1     1 Crossref
## 4   8722 1614 10.21105 The Open Journal               7     1 Crossref
## 5   8722  782 10.21105 The Open Journal               5     1 Crossref
## 6   8722   82 10.21105 The Open Journal               5     1 Crossref
##                                                                                                                    title
## 1                                                                              GB code: A grain boundary generation code
## 2                                                                            CRED: a rapid peak caller for Chem-seq data
## 3                                                                              carl: a likelihood-free inference toolbox
## 4                                                                  drms: A Python package for accessing HMI and AIA data
## 5             py-opc: operate the Alphasense OPC-N2 from a raspberry pi or other popular microcontrollers/microcomputers
## 6 Habfuzz: A tool to calculate the instream hydraulic habitat suitability using fuzzy logic and fuzzy Bayesian inference
##              type                                   url volume
## 1 journal-article http://dx.doi.org/10.21105/joss.00900      3
## 2 journal-article http://dx.doi.org/10.21105/joss.01423      4
## 3 journal-article http://dx.doi.org/10.21105/joss.00011      1
## 4 journal-article http://dx.doi.org/10.21105/joss.01614      4
## 5 journal-article http://dx.doi.org/10.21105/joss.00782      3
## 6 journal-article http://dx.doi.org/10.21105/joss.00082      1
##                                                                                                                                                                                                                                                                                                                                                                        author
## 1                                                                                                                                                                                                  http://orcid.org/0000-0002-9616-4602, http://orcid.org/0000-0003-4281-5665, NA, FALSE, FALSE, NA, R., B., J., Hadian, Grabowski, Neugebauer, first, additional, additional
## 2                                                                                          http://orcid.org/0000-0002-8086-3185, http://orcid.org/0000-0003-2358-7919, http://orcid.org/0000-0002-0916-7339, http://orcid.org/0000-0002-3992-5399, FALSE, FALSE, FALSE, FALSE, Jason, Tony, Paul, Hiroki, Lin, Kuo, Horton, Nagase, first, additional, additional, additional
## 3                                                                                                                                                            http://orcid.org/0000-0002-2082-3106, http://orcid.org/0000-0002-5769-7094, http://orcid.org/0000-0002-7205-0053, FALSE, FALSE, FALSE, Gilles, Kyle, Juan, Louppe, Cranmer, Pavez, first, additional, additional
## 4 http://orcid.org/0000-0002-1361-5712, http://orcid.org/0000-0002-5662-9604, http://orcid.org/0000-0001-6915-4583, http://orcid.org/0000-0002-0361-6463, http://orcid.org/0000-0003-4217-4642, FALSE, FALSE, FALSE, FALSE, FALSE, Kolja, Monica, Nitin, Arthur, Stuart, Glogowski, Bobra, Choudhary, Amezcua, Mumford, first, additional, additional, additional, additional
## 5                                                                                                                                                                                                                                  http://orcid.org/0000-0001-5111-4671, NA, NA, FALSE, NA, NA, David, Andrew, Jakub, H Hagan, Tolmie, Trochim, first, additional, additional
## 6                                                                                                                                                                                                             http://orcid.org/0000-0002-5395-0347, NA, NA, FALSE, NA, NA, Christos, Nikolaos, Anastasios, Theodoropoulos, Skoulikidis, Stamou, first, additional, additional
##   citation_count
## 1              1
## 2              1
## 3              3
## 4              2
## 5              1
## 6              7
##                                                                                                                api_title
## 1                                                                              GB code: A grain boundary generation code
## 2                                                                            CRED: a rapid peak caller for Chem-seq data
## 3                                                                              carl: a likelihood-free inference toolbox
## 4                                                                  drms: A Python package for accessing HMI and AIA data
## 5             py-opc: operate the Alphasense OPC-N2 from a raspberry pi or other popular microcontrollers/microcomputers
## 6 Habfuzz: A tool to calculate the instream hydraulic habitat suitability using fuzzy logic and fuzzy Bayesian inference
##   api_state                              repo_url review_issue_id
## 1  accepted  https://github.com/oekosheri/GB_code             900
## 2  accepted      https://github.com/jlincbio/cred            1423
## 3  accepted     https://github.com/diana-hep/carl              11
## 4  accepted         https://github.com/sunpy/drms            1614
## 5  accepted     https://github.com/dhhagan/py-opc             782
## 6  accepted https://github.com/chtheodoro/Habfuzz              82
##   prereview_issue_id                        languages
## 1                853                       Python,TeX
## 2               1374              Makefile,Perl,C,TeX
## 3                 NA            Python,Mako,Shell,TeX
## 4               1559                       Python,TeX
## 5                540                       Python,TeX
## 6                 77 Fortran,Shell,Batchfile,HTML,TeX
##                                archive_doi
## 1   https://doi.org/10.5281/zenodo.1433530
## 2   https://doi.org/10.5281/zenodo.2667613
## 3   http://dx.doi.org/10.5281/zenodo.47798
## 4   https://doi.org/10.5281/zenodo.3369966
## 5 http://dx.doi.org/10.5281/zenodo.1299205
## 6 https://dx.doi.org/10.5281/zenodo.163291
##                                                                                                 review_title
## 1                                                                  GB_code: A grain boundary generation code
## 2                                                                CRED: a rapid peak caller for Chem-seq data
## 3                                                                  carl: a likelihood-free inference toolbox
## 4                                                      drms: A Python package for accessing HMI and AIA data
## 5 py-opc: operate the Alphasense OPC-N2 from a raspberry pi or other popular microcontrollers/microcomputers
## 6       Habfuzz: A Fortran tool to calculate the instream hydraulic habitat suitability based on fuzzy logic
##   review_number review_state review_opened review_closed review_ncomments
## 1           900       closed    2018-08-17    2018-09-23               90
## 2          1423       closed    2019-05-01    2019-05-08               93
## 3            11       closed    2016-05-04    2016-05-11               36
## 4          1614       closed    2019-08-01    2019-08-20               62
## 5           782       closed    2018-06-18    2018-06-29               63
## 6            82       closed    2016-09-27    2016-10-27               41
##                         review_labels
## 1 accepted,published,recommend-accept
## 2 accepted,published,recommend-accept
## 3 accepted,published,recommend-accept
## 4 accepted,published,recommend-accept
## 5 accepted,published,recommend-accept
## 6 accepted,published,recommend-accept
##                                                                                                 prerev_title
## 1                                                                  GB_code: A grain boundary generation code
## 2                                                                CRED: a rapid peak caller for Chem-seq data
## 3                                                                                                       <NA>
## 4                                                      drms: A Python package for accessing HMI and AIA data
## 5 py-opc: operate the Alphasense OPC-N2 from a raspberry pi or other popular microcontrollers/microcomputers
## 6       Habfuzz: A Fortran tool to calculate the instream hydraulic habitat suitability based on fuzzy logic
##   prerev_state prerev_opened prerev_closed prerev_ncomments   prerev_labels
## 1       closed    2018-07-26    2018-08-17               42      Python,TeX
## 2       closed    2019-04-14    2019-05-01               30 C,Makefile,Perl
## 3         <NA>          <NA>          <NA>               NA            <NA>
## 4       closed    2019-07-11    2019-08-01               29          Python
## 5       closed    2018-01-19    2018-06-18               34      Python,TeX
## 6       closed    2016-09-21    2016-09-27               16                
##   days_in_pre days_in_rev to_review repo_created repo_updated repo_pushed
## 1     22 days     37 days      TRUE   2018-07-12   2020-07-06  2019-08-08
## 2     17 days      7 days      TRUE   2019-04-10   2020-02-26  2020-02-26
## 3     NA days      7 days      TRUE   2015-11-23   2020-07-22  2017-05-04
## 4     21 days     19 days      TRUE   2016-05-12   2020-04-16  2020-04-16
## 5    150 days     11 days      TRUE   2015-02-15   2020-06-04  2019-08-16
## 6      6 days     30 days      TRUE   2016-09-20   2020-02-06  2020-02-06
##   repo_nbr_stars repo_language repo_license published.date halfyear
## 1             11        Python          mit     2018-09-23   2018H2
## 2              1             C      gpl-3.0     2019-05-08   2019H1
## 3             55        Python bsd-3-clause     2016-05-11   2016H1
## 4             11        Python          mit     2019-08-20   2019H2
## 5             21        Python          mit     2018-06-29   2018H1
## 6              0       Fortran   apache-2.0     2016-10-27   2016H2

saveRDS(papers, file = "joss_submission_analytics.rds")

Session info

sessionInfo()

## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.6
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] plotly_4.9.2.1  DT_0.14         jsonlite_1.7.0  purrr_0.3.4    
##  [5] gh_1.1.0        lubridate_1.7.9 ggplot2_3.3.2   tidyr_1.1.1    
##  [9] dplyr_1.0.1     rcrossref_1.0.0 tibble_3.0.3   
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_1.0.5         lattice_0.20-41    assertthat_0.2.1   digest_0.6.25     
##  [5] mime_0.9           R6_2.4.1           plyr_1.8.6         evaluate_0.14     
##  [9] httr_1.4.2         pillar_1.4.6       rlang_0.4.7        lazyeval_0.2.2    
## [13] curl_4.3           rstudioapi_0.11    data.table_1.13.0  miniUI_0.1.1.1    
## [17] Matrix_1.2-18      rmarkdown_2.3      splines_4.0.2      urltools_1.7.3    
## [21] labeling_0.3       stringr_1.4.0      htmlwidgets_1.5.1  triebeard_0.3.0   
## [25] munsell_0.5.0      shiny_1.5.0        compiler_4.0.2     httpuv_1.5.4      
## [29] xfun_0.16          pkgconfig_2.0.3    mgcv_1.8-31        htmltools_0.5.0   
## [33] tidyselect_1.1.0   httpcode_0.3.0     fansi_0.4.1        viridisLite_0.3.0 
## [37] crayon_1.3.4       withr_2.2.0        later_1.1.0.1      crul_1.0.0        
## [41] grid_4.0.2         nlme_3.1-148       xtable_1.8-4       gtable_0.3.0      
## [45] lifecycle_0.2.0    magrittr_1.5       scales_1.1.1       bibtex_0.4.2.2    
## [49] cli_2.0.2          stringi_1.4.6      farver_2.0.3       promises_1.1.1    
## [53] xml2_1.3.2         ellipsis_0.3.1     generics_0.0.2     vctrs_0.3.2       
## [57] RColorBrewer_1.1-2 tools_4.0.2        glue_1.4.1         crosstalk_1.1.0.1 
## [61] fastmap_1.0.1      yaml_2.2.1         colorspace_1.4-1   knitr_1.29

JOSS submission analytics

2020-08-03 20:36:41